Low-Latency Coded Tensor--Matrix Multiplication for Distributed Signal Processing Systems
Abstract
Large-scale signal processing systems, including aeronautical and aerospace platforms, increasingly rely on tensor operations, whose distributed execution is constrained by communication, memory, and latency bottlenecks. In this paper, we propose a tensor-aware coded computation framework for distributed mode-1 tensor--matrix multiplication that operates directly on tensor subtensors, avoiding explicit unfolding and preserving multi-dimensional structure. For a fixed partitioning configuration, the proposed tensor-PolyDot scheme achieves the same recovery threshold, communication cost, worker-side computation, and memory requirements as conventional matrix-based PolyDot schemes. However, by leveraging multivariate encoding, it replaces high-degree univariate interpolation at the fusion node with structured low-dimensional decoding. This leads to a substantial reduction in decoding complexity and latency, without affecting any other system metric. Additionally, the tensor-based approach improves memory locality and enables parallel decoding across tensor modes, making it well-suited to modern hardware architectures. Numerical results show decoding speedups proportional to the tensor partitioning factor, reaching up to an order-of-magnitude improvement in practical settings. These advantages make the proposed framework particularly relevant to latency-critical aeronautical and aerospace distributed signal processing systems.
Related
- Improved Degree Tables for Secure Distributed Matrix Multiplication
- Tensor Degenerations for Matrix Multiplication
- Latency-Optimal Geo-Distributed Storage over Structured Networks
- Simultaneous Matrix Multiplication by Tensor Contraction Aggregation
- Function Tables for Secure Distributed Matrix Multiplication
BibTeX
@misc{tanha2026lowlatency,
title = {{Low-Latency Coded Tensor--Matrix Multiplication for Distributed Signal Processing Systems}},
author = {Ahmad Tanha and Ali Khalesi},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30567},
eprint = {2609.30567},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30567},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Low-Latency Coded Tensor--Matrix Multiplication for Distributed Signal Processing Systems
AU - Ahmad Tanha
AU - Ali Khalesi
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.30567
UR - https://arxiv.org/abs/2609.30567
AB - Large-scale signal processing systems, including aeronautical and aerospace platforms, increasingly rely on tensor operations, whose distributed execution is constrained by communication, memory, and latency bottlenecks. In this paper, we propose a tensor-aware coded computation framework for distributed mode-1 tensor--matrix multiplication that operates directly on tensor subtensors, avoiding explicit unfolding and preserving multi-dimensional structure. For a fixed partitioning configuration, the proposed tensor-PolyDot scheme achieves the same recovery threshold, communication cost, worker-side computation, and memory requirements as conventional matrix-based PolyDot schemes. However, by leveraging multivariate encoding, it replaces high-degree univariate interpolation at the fusion node with structured low-dimensional decoding. This leads to a substantial reduction in decoding complexity and latency, without affecting any other system metric. Additionally, the tensor-based approach improves memory locality and enables parallel decoding across tensor modes, making it well-suited to modern hardware architectures. Numerical results show decoding speedups proportional to the tensor partitioning factor, reaching up to an order-of-magnitude improvement in practical settings. These advantages make the proposed framework particularly relevant to latency-critical aeronautical and aerospace distributed signal processing systems.
ER -